EEG Technologists Research Articles

EEG Infrastructure Within the Veterans Administration: A Survey.

EEG is a vital tool in the diagnosis and management of neurologic conditions prevalent among veterans such as seizures, epilepsy, and brain injuries. This cross-sectional study aimed to assess the state of EEG infrastructure within the Veterans Administration (VA), focusing on availability, utilization, and the potential avenues to addressing gaps in infrastructure. This survey was distributed to 123 VA hospitals using the Research Electronic Data Capture (REDCap) platform, gathering data on EEG equipment, staffing, and service provision from June to December 2023. Of the 123 VA hospitals surveyed, 70 responded (56.9% response rate). Most respondents (88.6%) reported having EEG services, although only 38.7% offering continuous EEG (cEEG). Respondents reported having less EEG technologists, machines, and faculty readers than what they thought would be ideal. Significant correlations were found between the availability of resources (e.g., number of EEG machines) and service capabilities, including remote access and cEEG. The use of alternative EEG technologies such as rapid or quantitative EEG varied greatly. Interest in participating in the VA Tele-EEG program was reported by 59.4% of respondents. There is large variability in EEG infrastructure across the VA. Tele-EEG has the potential to maintain continuity of operations through challenges affecting staffing and to improve EEG service access, especially in resource-limited settings. Expanding access to quantitative, rapid, and tele-EEG services may enhance patient management and may be a potential avenue to explore as the VA continues to invest in and grow its capacity for treating neurologic conditions.

Intervention time and adverse events in a canadian epilepsy monitoring unit: An updated audit

BackgroundOptimizing patient safety in the epilepsy monitoring unit (EMU) has become a topic of increasing interest. We performed an audit of our center's new single-floor EMU, assessing intervention rate (IR), intervention time (IT), and adverse events (AEs). MethodsA prospective study was conducted on all clinical seizures of patients admitted over a one-year period at our Canadian academic tertiary care center's new single-floor EMU. This single-floor EMU was supervised by EEG technologists during daytime (similar to the old set-up) and beneficiary attendants during nighttime/weekends (versus live video feed to the central nursing station on the neurology ward previously). Among 153 admissions, 79 were analyzed, and a total of 537 seizures were reviewed to assess IR, IT, and AEs. Univariate comparisons were performed with our double-floor EMU, which we reported in a previous publication. ResultsIn our new single-floor EMU, the IR was 61.1 % and overall median IT was 29.0s (19.0s–45.9s). The AE rate was 4.8 %. Compared to previously reported numbers for our old double-floor EMU (IR = 27.8 %; IT = 21.0s; AE = 1.2 %), the IR was significantly higher ((p < 0.001) but unexpectedly, the median IT was higher (p < 0.001) as well as the AE rate (p < 0.001). ConclusionThis prospective evaluation revealed a small but non-negligible rate of complications in our EMU, higher than our prior retrospective audit. Heightened levels of supervision in our new single-floor EMU led to higher IR. This may have led to artificially longer ITs.

Assessing expert reliability in determining intracranial EEG channel quality and introducing the automated bad channel detection algorithm

Objective. To evaluate the inter- and intra-rater reliability for the identification of bad channels among neurologists, EEG Technologists, and naïve research personnel, and to compare their performance with the automated bad channel detection (ABCD) algorithm for detecting bad channels. Approach. Six Neurologists, ten EEG Technologists, and six naïve research personnel (22 raters in total) were asked to rate 1440 real intracranial EEG channels as good or bad. Intra- and interrater kappa statistics were calculated for each group. We then compared each group to the ABCD algorithm which uses spectral and temporal domain features to classify channels as good or bad. Main results. Analysis of channel ratings from our participants revealed variable intra-rater reliability within each group, with no significant differences across groups. Inter-rater reliability was moderate among neurologists and EEG Technologists but minimal among naïve participants. Neurologists demonstrated a slightly higher consistency in ratings than EEG Technologists. Both groups occasionally misclassified flat channels as good, and participants generally focused on low-frequency content for their assessments. The ABCD algorithm, in contrast, relied more on high-frequency content. A logistic regression model showed a linear relationship between the algorithm’s ratings and user responses for predominantly good channels, but less so for channels rated as bad. Sensitivity and specificity analyses further highlighted differences in rating patterns among the groups, with neurologists showing higher sensitivity and naïve personnel higher specificity. Significance. Our study reveals the bias in human assessments of intracranial electroencephalography (iEEG) data quality and the tendency of even experienced professionals to overlook certain bad channels, highlighting the need for standardized, unbiased methods. The ABCD algorithm, outperforming human raters, suggests the potential of automated solutions for more reliable iEEG interpretation and seizure characterization, offering a reliable approach free from human biases.

Seizure Assessment and Forecasting With Efficient Rapid-EEG: A Retrospective Multicenter Comparative Effectiveness Study.

Approximately 30% of critically ill patients have seizures, and more than half of these seizures do not have an overt clinical correlate. EEG is needed to avoid missing seizures and prevent overtreatment with antiseizure medications. Conventional-EEG (cEEG) resources are logistically constrained and unable to meet their growing demand for seizure detection even in highly developed centers. Brief EEG screening with the validated 2HELPS2B algorithm was proposed as a method to triage cEEG resources, but it is hampered by cEEG requirements, primarily EEG technologists. Seizure risk-stratification using reduced time-to-application rapid response-EEG (rrEEG) systems (∼5 minutes) could be a solution. We assessed the noninferiority of the 2HELPS2B score on a 1-hour rrEEG compared to cEEG. A multicenter retrospective EEG diagnostic accuracy study was conducted from October 1, 2021, to July 31, 2022. Chart and EEG review performed with consecutive sampling at 4 tertiary care centers, included records of patients ≥18 years old, from January 1, 2018, to June 20, 2022. Monte Carlo simulation power analysis yielded n = 500 rrEEG; for secondary outcomes n = 500 cEEG and propensity-score covariate matching was planned. Primary outcome, noninferiority of rrEEG for seizure risk prediction, was assessed per area under the receiver operator characteristic curve (AUC). Noninferiority margin (0.05) was based on the 2HELPS2B validation study. A total of 240 rrEEG with follow-on cEEG were obtained. Median age was 64 (interquartile range 22); 42% were female. 2HELPS2B on a 1-hour rrEEG met noninferiority to cEEG (AUC 0.85, 95% CI 0.78-0.90, p = 0.001). Secondary endpoints of comparison with a matched contemporaneous cEEG showed no significant difference in AUC (0.89, 95% CI 0.83-0.94, p = 0.31); in false negative rate for the 2HELPS2B = 0 group (p = 1.0) rrEEG (0.021, 95% CI 0-0.062), cEEG (0.016, 95% CI 0-0.048); nor in survival analyses. 2HELPS2B on 1-hour rrEEG is noninferior to cEEG for seizure prediction. Patients with low-risk (2HELPS2B = 0) may be able to forgo prolonged cEEG, allowing for increased monitoring of at-risk patients. This study provides Class II evidence that rrEEG is noninferior to cEEG in calculating the 2HELPS2B score to predict seizure risk.

Survey of Continuous EEG Monitoring Practices in the United States.

Continuous EEG (cEEG) practice has markedly changed over the last decade given its utility in improving critical care outcomes. However, there are limited data describing the current cEEG infrastructure in US hospitals. A web-based cEEG practice survey was sent to neurophysiologists at 123 ACGME-accredited epilepsy or clinical neurophysiology programs. Neurophysiologists from 100 (81.3%) institutions completed the survey. Most institutions had 3 to 10 EEG faculty (80.0%), 1 to 5 fellows (74.8%), ≥6 technologists (84.9%), and provided coverage to neurology ICUs with >10 patients (71.0%) at a time. Round-the-clock EEG technologist coverage was available at most (90.0%) institutions with technologists mostly being in-house (68.0%). Most institutions without after-hours coverage (8 of 10) attributed this to insufficient technologists. The typical monitoring duration was 24 to 48 hours (23.0 and 40.0%), most commonly for subclinical seizures (68.4%) and spell characterization (11.2%). Larger neurology ICUs had more EEG technologists ( p = 0.02), fellows ( p = 0.001), and quantitative EEG use ( p = 0.001). This survey explores current cEEG practice patterns in the United States. Larger centers had more technologists and fellows. Overall technologist numbers are stable over time, but with a move toward more in-hospital compared with home-based coverage. Reduced availability of EEG technologists was a major factor limiting cEEG availability at some centers.

The Cost of After-Hour Electroencephalography.

High costs associated with after-hour electroencephalography (EEG) constitute a barrier for financially constrained hospitals to provide this neurodiagnostic procedure outside regular working hours. Our study aims to deepen our understanding of the cost elements involved in delivering EEG services during after-hours. We accessed publicly available data sets and created a cost model depending on 3 most commonly seen staffing scenarios: (1) technologist on-site, (2) technologist on-call from home, and (3) a hybrid of the two. Cost of EEG depends on the volume of testing and the staffing plan. Within the various cost elements, labor cost of EEG technologists is the predominant expenditure, which varies across geographic regions and urban areas. We provide a model to explain why access to EEGs during after-hours has a substantial expense. This model provides a cost calculator tool (made available as part of this publication in eAppendix 1, links.lww.com/CPJ/A513) to estimate the cost of EEG platform based on site-specific staffing scenarios and annual volume.

Education Research: Competency-Based EEG Education: An Online Routine EEG Examination for Adult and Child Neurology Residents.

We recently published expert consensus-based curricular objectives for routine EEG (rEEG) interpretation for adult and child neurology residents. In this study, we used this curriculum framework to develop and validate an online, competency-based, formative and summative rEEG examination for neurology residents. We developed an online rEEG examination consisting of a brief survey and 30 multiple-choice questions covering EEG learning objectives for neurology residents in 4 domains: normal, abnormal, normal variants, and artifacts. Each question contained a deidentified EEG image, displayed in 2 montages (bipolar and average), reviewed and optimized by the authors to address the learning objectives. Respondents reported their level of confidence (LOC, 5-point Likert scale) with identifying 4 categories of EEG findings independently: states of wakefulness/sleep, sleep structures, normal variants, and artifacts. Accuracy and item discrimination were calculated for each question and LOC for each category. The test was disseminated by the International League Against Epilepsy and shared on social media. Of 2,080 responses, 922 were complete. Respondents comprised clinical neurophysiologists/experts (n = 41), EEG/epilepsy clinical fellows (n = 211), EEG technologists (n = 128), attending neurologists (n = 111), adult neurology residents (n = 227), child neurology residents (n = 108), medical students (n = 24), attending non-neurologists (n = 18), and others (n = 54). Mean overall scores (95% CI) were 82% (77-86) (clinical neurophysiologists), 81% (79-83) (clinical fellows), and 72% (70-73) (adult and child neurology residents). Experts were more confident than clinical fellows in all categories but sleep structures. Experts and clinical fellows were more confident than residents in all 4 categories. Among residents, accuracy and LOC increased as a function of prior EEG weeks of training. Accuracy improved from 67% (baseline/no prior EEG training) to 77% (>12 prior EEG weeks). More than 8 weeks of EEG training was needed to reach accuracy comparable with clinical neurophysiologists on this rEEG examination. Increase in LOC was slower and less robust than increase in accuracy. All but 3 questions had a high discrimination index (>0.25). This online, competency-based rEEG examination, mapped to a published EEG curriculum, has excellent psychometrics and differentiates experienced EEG readers from adult and child neurology residents. This online tool has the potential to improve resident EEG education worldwide.

Curriculum Innovations: Developing an EEG Workshop for Hands-on Training in Electroencephalography During Resident Rotation in Clinical Neurophysiology

ABSTRACT A 3-hour hands-on EEG workshop was conducted as a part of resident rotation in clinical neurophysiology to determine its feasibility and educational value for neurology residents, medical students, and EEG technologists, with the learning objectives of motivating interest in clinical electroencephalography, enhancing ability to recognize a normal EEG and identify physiological and non-physiological artifacts, and enhancing teaching, organizational and communication skills of EEG technologists. The workshop included a demonstration of EEG electrode placement according to the 10–20 System on a resident volunteer by the EEG technologist. Activation procedures (photic stimulation and hyperventilation) were demonstrated to the residents during the 25-minute recording. A board-certified epileptologist later reviewed the record with the residents. Resident and EEG technologist feedback on the workshop was obtained via non-formal e-mail request. All residents rated the workshop highly and found it more engaging and motivating than the didactic EEG lecture in which basics of EEG recording are reviewed. The residents gained improved awareness and appreciation of the role of the technologist and the skill and time involved in obtaining an EEG record. The EEG technologist appreciated the face-to-face interaction with the residents and felt part of the patient care team. A hands-on EEG workshop as a part of resident clinical neurophysiology rotation is feasible, effective, and motivating in teaching residents and medical students the basis of EEG recording and recognition of common physiological and non-physiological artifacts in a normal record.

Ellen R. Grass Lecture: The Future of Neurodiagnostics and Emergence of a New Science

ABSTRACT Electroencepholography (EEG) is the oldest and original brain measurement technology. Since EEG was first used in clinical settings, the role of neurodiagnostic professionals has focused on two principal tasks that require specialized training. These include collecting the EEG recording, performed primarily by EEG Technologists, and interpreting the recording, generally done by physicians with proper specialization. Emerging technology appears to enable non-specialists to contribute to these tasks. Neurotechnologists may feel vulnerable to being displaced by new technology. A similar shift occurred in the last century when human “computers,” employed to perform repetitive calculations needed to solve complex mathematics for the Manhattan and Apollo Projects, were displaced by new electronic computing machines. Many human “computers” seized on the opportunity created by the new computing technology to become the first computer programmers and create the new field of computer science. That transition offers insights for the future of neurodiagnostics. From its inception, neurodiagnostics has been an information processing discipline. Advances in dynamical systems theory, cognitive neuroscience, and biomedical informatics have created an opportunity for neurodiagnostic professionals to help create a new science of functional brain monitoring. A new generation of advanced neurodiagnostic professionals that bring together knowledge and skills in clinical neuroscience and biomedical informatics will benefit psychiatry, neurology, and precision healthcare, lead to preventive brain health through the lifespan, and lead the establishment of a new science of clinical neuroinformatics.

EEG as an indispensable tool during and after the COVID-19 pandemic: A review of tribulations and successes.

During the coronavirus disease 2019 (COVID-19) pandemic, elective and non-emergent tests and procedures were delayed or suspended in lieu of diverting resources to more emergent treatment of critically ill patients and to avoid the spread and contraction of COVID-19. Further, the workforce was stretched thin, and healthcare facilities saw high turnover rates for full-time and contract employees, which strained the system and reduced the ability to provide clinical services. One of the casualties of these changes was electroencephalography (EEG) procedures, which have been performed less frequently throughout the world since the pandemic. Whether considered routine or emergent, the deferral of EEG studies can cause downstream effects, including a delay in diagnosis and initiation of treatment for epilepsy and non-epileptic seizures resulting in a higher risk of morbidity and mortality. Despite these limitations, the importance and utility of EEG and EEG technologists have been reinforced with the development of COVID-related neurological complications, including encephalopathy and seizures, which require EEG for diagnosis and treatment. Since the pandemic, reliance on remote telemonitoring has further highlighted the value and ease of using EEG. There has also been a heightened interest in rapid EEG devices that non-technologist professionals can attach quickly, allowing minimum patient contact to avoid exposure to COVID-19 and taking advantage of remote EEG monitoring. This review discusses the acute and potential long-term effects of the COVID-19 pandemic on the use and performance of EEG.

Estimate of Patients With Missed Seizures Because of Delay in Conventional EEG.

There is frequent delay between ordering and placement of conventional EEG. Here we estimate how many patients had seizures during this delay. Two hundred fifty consecutive adult patients who underwent conventional EEG monitoring at the University of Wisconsin Hospital were retrospectively chart reviewed for demographics, time of EEG order, clinical and other EEG-related information. Patients were stratified by use of anti-seizure medications before EEG and into low-risk, medium-risk, and high-risk groups based on 2HELPS2B score (0, 1, or >1). Monte Carlo simulations (500 trials) were performed to estimate seizures during delay. The median delay from EEG order to performing EEG was 2.00 hours (range of 0.5-8.00 hours) in the total cohort. For EEGs ordered after-hours, it was 2.00 hours (range 0.5-8.00 hours), and during business hours, it was 2.00 hours (range 0.5-6.00 hours). The place of EEG, intensive care unit, emergency department, and general floor, did not show significant difference (P = 0.84). Anti-seizure medication did not affect time to first seizure in the low-risk (P = 0.37), medium-risk (P = 0.44), or high-risk (P = 0.12) groups. The estimated % of patients who had a seizure in the delay period for low-risk group (2HELPS2B = 0) was 0.8%, for the medium-risk group (2HELPS2B = 1) was 10.3%, and for the high-risk group (2HELPS2B > 1) was 17.6%, and overall risk was 7.2%. The University of Wisconsin Hospital with 24-hour in-house EEG technologists has a median delay of 2 hours from order to start of EEG, shorter than published reports from other centers. Nonetheless, seizures were likely missed in about 7.2% of patients.

EEG Pattern in Neonatal Maple Syrup Urine Disease: Description and Clinical Significance

ABSTRACT Maple Syrup Urine Disease (MSUD) is a rare autosomal recessive disorder characterized by deficiency of branched-chain keto acid dehydrogenase complex, which is required to metabolize the three branched chain amino acids (BCAAs), leucine, isoleucine and valine. This metabolic dysfunction results in progressive encephalopathy manifesting with lethargy, vomiting, posturing and abnormal movements during the neonatal period in the classic form of the disease. If untreated, progressive brain damage causes coma, seizures and death usually within a few weeks. EEG is an essential investigation in a neonate with progressive encephalopathy and seizures. EEG abnormalities in neonatal encephalopathies due to inborn error of metabolism (IEM) are widely variable depending on the severity. Central comb-like rhythm is an EEG marker of neonatal MSUD in appropriate clinical context. This pattern should not be mistaken for epileptic abnormalities, sleep spindles or other similar nonspecific activities. We describe a patient with classic MSUD who presented with an EEG pattern of comb-like rhythm. Background abnormalities and epileptic discharges are common along comb-like rhythm in MSUD patients. EEG technologists and interpreters should be able to identify this pattern to support the early diagnosis and treatment of MSUD.

Vasovagal Syncope in Epilepsy Monitoring Unit: A Case Report and Review

ABSTRACT Falls are a common adverse event (AE) in general hospital admissions and in the epilepsy monitoring unit (EMU) and can cause serious physical injury for patients; however, fall prevention policies widely vary between epilepsy centers. Patient safety, including minimizing AEs, such as falls, continue to be a major goal of quality improvement in the EMU. The bathroom area in an EMU is highly prone for falls due to the need for patient privacy. Syncope is a physiologic non-epileptic event with high prevalence in general population and is a potential cause for fall. It is also reported in people diagnosed with epilepsy. We report a case of vasovagal syncope (VVS) associated with injury in a young female during long-term video EEG recording in our EMU facility. We also briefly address concerns for identifying patients at risk for syncope and its prevention. A multidisciplinary team including EEG technologists can play a vital role in educating patients and caretakers about the risk factors of syncopal fall and its preventive measures.

Prospective evaluation of interrater agreement between EEG technologists and neurophysiologists

We aim to prospectively investigate, in a large and heterogeneous population, the electroencephalogram (EEG)-reading performances of EEG technologists. A total of 8 EEG technologists and 5 certified neurophysiologists independently analyzed 20-min EEG recordings. Interrater agreement (IRA) for predefined EEG pattern identification between EEG technologists and neurophysiologits was assessed using percentage of agreement (PA) and Gwet-AC1. Among 1528 EEG recordings, the PA [95% confidence interval] and interrater agreement (IRA, AC1) values were as follows: status epilepticus (SE) and seizures, 97% [96–98%], AC1 kappa = 0.97; interictal epileptiform discharges, 78% [76–80%], AC1 = 0.63; and conclusion dichotomized as “normal” versus “pathological”, 83.6% [82–86%], AC1 = 0.71. EEG technologists identified SE and seizures with 99% [98–99%] negative predictive value, whereas the positive predictive values (PPVs) were 48% [34–62%] and 35% [20–53%], respectively. The PPV for normal EEGs was 72% [68–76%]. SE and seizure detection were impaired in poorly cooperating patients (SE and seizures; p < 0.001), intubated and older patients (SE; p < 0.001), and confirmed epilepsy patients (seizures; p = 0.004). EEG technologists identified ictal features with few false negatives but high false positives, and identified normal EEGs with good PPV. The absence of ictal features reported by EEG technologists can be reassuring; however, EEG traces should be reviewed by neurophysiologists before taking action.

Impact of the COVID-19 Pandemic on Continuous EEG Utilization.

The coronavirus disease 2019 (COVID-19) has significantly impacted healthcare delivery and utilization. The aim of this article was to assess the impact of the COVID-19 pandemic on in-hospital continuous electroencephalography (cEEG) utilization and identify areas for process improvement. A 38-question web-based survey was distributed to site principal investigators of the Critical Care EEG Monitoring Research Consortium, and institutional contacts for the Neurodiagnostic Credentialing and Accreditation Board. The survey addressed the following aspects of cEEG utilization: (1) general center characteristics, (2) cEEG utilization and review, (3) staffing and workflow, and (4) health impact on EEG technologists. The survey was open from June 12, 2020 to June 30, 2020 and distributed to 174 centers with 79 responses (45.4%). Forty centers were located in COVID-19 hotspots. Fifty-seven centers (72.1%) reported cEEG volume reduction. Centers in the Northeast were most likely to report cEEG volume reduction (odds ratio [OR] 7.19 [1.53-33.83]; P = 0.012). Additionally, centers reporting decrease in outside hospital transfers reported cEEG volume reduction; OR 21.67 [4.57-102.81]; P ≤ 0.0001. Twenty-six centers (32.91%) reported reduction in EEG technologist coverage. Eighteen centers had personal protective equipment shortages for EEG technologists. Technologists at these centers were more likely to quarantine for suspected or confirmed COVID-19; OR 3.14 [1.01-9.63]; P = 0.058. There has been a widespread reduction in cEEG volume during the pandemic. Given the anticipated duration of the pandemic and the importance of cEEG in managing hospitalized patients, methods to optimize use need to be prioritized to provide optimal care. Because the survey provides a cross-sectional assessment, follow-up studies can determine the long-term impact of the pandemic on cEEG utilization.

Electroencephalography Technologist Inter-rater Agreement and Interpretation of Pediatric Critical Care Electroencephalography

ObjectivesElectroencephalography (EEG) technologists commonly screen continuous EEG. Until now, the inter-rater agreement or sensitivity for important EEG findings has been unknown in this group. MethodsTwenty-nine EEG technologists and three clinical neurophysiologists interpreted 90 five-minute samples of pediatric critical care EEG. Inter-rater agreement was examined with Cohen’s kappa and Fleiss’ kappa for EEG findings. A gold-standard consensus agreement was developed for examining sensitivity and specificity for seizures or discontinuity. Kruskal-Wallis tests with Benjamani-Hochberg corrections for multiple comparisons were utilized to examine associations between correct scoring and certification status and years of experience. ResultsAggregate agreement was moderate for seizures and fair for EEG background continuity among EEG technologists. Individual agreement for seizures and continuity varied from slight to substantial. For individual EEG technologists, sensitivity for seizures ranged from 44 to 93% and sensitivity for continuity ranged from 81 to 100%. Raters with Certified Long Term Monitoring credentials were more likely to identify seizures correctly. SignificanceThis is the first study to evaluate inter-rater agreement and interpretation correctness among EEG technologists interpreting pediatric critical care EEG. EEG technologists demonstrated better aggregate agreement for seizure detection than other EEG findings, yet individual results and internal consistency varied widely. These data provide important insight into the common practice of utilizing EEG technologists for screening critical care EEG.

The Yield of Ambulatory EEG-Video Monitoring.

The availability of ambulatory EEG-video monitoring gives an alternative to traditional inpatient EEG-video, but its yield and diagnostic value are not well known. This study evaluates the yield of ambulatory EEG-video for the diagnosis of epilepsy. We retrospectively reviewed the ambulatory EEG-video monitoring data from 200 consecutive and unselected patients aged 12 years and older performed by a single company (RSC Diagnostic Services) between January 2018 and May 2018. Studies were processed by two senior certified long-term monitoring EEG technologists and interpreted by neurologists. Of the 200 patients, 130 (65%) were women, mean age was 45 years. Mean duration of studies were 76.6 hours (range 23-175 hours). There were 110 studies (55%) with events recorded and 101 (92%) were captured on video. Epileptic events accounted for 17.8% (18/101) of the events captured and 9% (18/200) of our total cohort. Nonepileptic diagnosis accounted for 38% of the total number of patients in study (76/200). Ambulatory EEG-video monitoring may be a useful alternative to inpatient epilepsy monitoring unit, particularly with high clinical suspicion for nonepileptic events.

Rapid Response Electroencephalography for Urgent Evaluation of Patients in Community Hospital Intensive Care Practice.

Limited access to specialized technicians and trained neurologists results in delayed access to electroencephalography (EEG) and an accurate diagnosis of patients with critical neurological problems. This study evaluated the performance of Ceribell Rapid Response EEG System (RR-EEG), which promises fast EEG acquisition and interpretation without traditional technicians or EEG-trained specialists. The new technology was tested in a community hospital intensive care unit in Northern California. Three physicians (without previous training in EEG) were trained by the manufacturer of the RR-EEG and acquired EEG without the help of any EEG technicians. Time needed from order to EEG acquisition was noted. Quality of EEG and diagnostic information obtained with the new EEG technology were evaluated and compared with the same information from conventional clinical EEG system. Ten patients were tested with this new EEG technology, and 6 of these patients went on to have conventional EEGs when the EEG technicians arrived at the site. In these cases, the conventional EEG was significantly delayed (11.2 ± 3.6 hours) compared with RR-EEG (5.0 ± 2.4 minutes; P < .005). Use of RR-EEG helped clinicians rule out status epilepticus and prevent overtreatment in 4 of 10 cases. RR-EEG and conventional EEG systems yielded similar diagnostic information. RR-EEG can be set up by nurses, and diagnostic information about the presence or absence of seizures can be appreciated by nurses. The RR-EEG system, compared with the conventional EEG, did not require EEG technologists and enabled significantly faster access to diagnostic EEG information. This report confirms the ease of use and speed of acquisition and interpretation of EEG information at a community hospital setting using an RR-EEG device. This new technology has the potential to improve emergent clinical decision making and prevent overtreatment of patients in the intensive care unit setting while empowering nursing staff with useful diagnostic information in real time and at the bedside.

Use of Continuous EEG Monitoring in Children Presenting With Encephalopathy Following Convulsive Status Epilepticus.

The Critical Care Continuous EEG Task Force of the American Clinical Neurophysiology Society recommends continuous EEG (cEEG) monitoring in patients with persistent encephalopathy following convulsive status epilepticus. This recommendation is based on data, which correlates prolonged nonconvulsive seizures and nonconvulsive status epilepticus with worse neurologic outcomes. Compliance with these recommendations may be limited by barriers such as inadequate resource and staff availability. We surveyed members of the Child Neurology Society to determine the barriers that prevent them from appropriately using cEEG, and how they have successfully overcome such obstacles. A survey was electronically distributed to Child Neurology Society members, which assessed demographics, current clinical practices, and cEEG utilization in critically ill children, with an emphasis on resource availability and strategies to overcome resource limitations. One hundred forty-six physicians from Child Neurology Society completed the survey. Fifty-three (39.8%) respondents use cEEG to detect nonconvulsive seizures/nonconvulsive status epilepticus in most (>90%) of their pediatric patients who present with persistent encephalopathy following convulsive status epilepticus. Forty-four respondents (34.4%) perceive barriers to performing cEEG monitoring, and 107 (84.9%) of the respondents are implementing changes to overcome barriers. The two most commonly reported barriers included inadequate availability of technicians and EEG machines. The most common changes included hiring new EEG technologists and purchasing new machines. Other barriers included identification of appropriate patients and availability of remote EEG monitoring capabilities. Barriers, such as resource limitations, prevent compliance with the American Clinical Neurophysiology Society cEEG monitoring recommendations. Recognizing common limitations and learning from each other about successful strategies to overcome these barriers may improve care.

Comparing the quality of signals recorded with a rapid response EEG and conventional clinical EEG systems

ObjectivesTo compare the quality of electroencephalography (EEG) signals recorded with a rapid response EEG system and the signals recorded with conventional clinical EEG recordings. MethodsWe studied the differences between EEG recordings taken with a rapid response EEG system (Ceribell) compared to conventional EEG through two separate set of studies. First, we conducted simultaneous recording on a healthy subject in an experimental laboratory setting where the rapid response EEG and two conventional EEG recording systems (Nihon Kohden and Natus) were used at the same time on the same subject using separate but adjacently placed electrodes. The rapid response EEG was applied by a user without prior training in EEG set up while two separate sets of conventional EEG electrodes were placed by a trained EEG technologist. The correlation between each of the recordings was calculated and quantitatively compared. In the second study, we performed a set of consecutive recordings on 22 patients in an ICU environment. The rapid response EEG system was applied by clinical ICU fellows without prior training in EEG set up while waiting for the conventional EEG system to arrive, after which the rapid response EEG was stopped and the conventional EEG was applied by a trained EEG technologist. We measured and compared several metrics of EEG quality using comparative metrics. ResultsFor the simultaneous recording performed in a laboratory environment, the tested rapid response EEG and conventional EEG recordings showed agreement when aligned and visually compared in the time domain, all EEG waveform features were distinguishable in both recordings. The correlation between each pair of recordings also showed that the correlation between the rapid response EEG recording and each of the two conventional recordings was statistically the same as the correlation between the two conventional recordings. For the consecutive recordings performed in real life clinical ICU environment, Hjorth parameters, spike count, baseline wander, and kurtosis measures were statistically similar (p > 0.05, Wilcoxon signed rank test) for the rapid response EEG and conventional clinical EEG recordings. The rapid response EEG data had significantly lower 60 Hz noise compared to recordings made with the conventional systems both in laboratory and ICU settings. Lastly, the clinical information obtained with the rapid response EEG system was concordant with the diagnostic information obtained with the conventional EEG recordings in the ICU setting. ConclusionsOur findings show that the tested rapid response EEG system provides EEG recording quality that is equivalent to conventional EEG systems and even better when it comes to 60 Hz noise level. The concordance between the rapid response EEG and conventional EEG systems was demonstrated both in a controlled laboratory environment as well as in the noisy environment of a hospital ICU on patients with altered mental status. SignificanceOur findings clearly confirm that the tested rapid response EEG system provides EEG data that is equivalent in quality to the recordings made using conventional EEG systems despite the fact that the rapid response system can be applied within few minutes and with no reliance on specialized technologists. This can be important for urgent situations where the use of conventional EEG systems is hindered by the lengthy setup time and limited availability of EEG technologists.